def test_extract_obs_data():
with pytest.raises(TypeError):
extract_obs_data(at.matrix())
data = np.random.normal(size=(2, 3))
data_at = at.as_tensor(data)
mask = np.random.binomial(1, 0.5, size=(2, 3)).astype(bool)
for val_at in (data_at, aesara.shared(data)):
res = extract_obs_data(val_at)
assert isinstance(res, np.ndarray)
assert np.array_equal(res, data)
# AdvancedIncSubtensor check
data_m = np.ma.MaskedArray(data, mask)
missing_values = data_at.type()[mask]
constant = at.as_tensor(data_m.filled())
z_at = at.set_subtensor(constant[mask.nonzero()], missing_values)
assert isinstance(z_at.owner.op,
(AdvancedIncSubtensor, AdvancedIncSubtensor1))
res = extract_obs_data(z_at)
assert isinstance(res, np.ndarray)
assert np.ma.allequal(res, data_m)
# AdvancedIncSubtensor1 check
data = np.random.normal(size=(3, ))
data_at = at.as_tensor(data)
mask = np.random.binomial(1, 0.5, size=(3, )).astype(bool)
data_m = np.ma.MaskedArray(data, mask)
missing_values = data_at.type()[mask]
constant = at.as_tensor(data_m.filled())
z_at = at.set_subtensor(constant[mask.nonzero()], missing_values)
assert isinstance(z_at.owner.op,
(AdvancedIncSubtensor, AdvancedIncSubtensor1))
res = extract_obs_data(z_at)
assert isinstance(res, np.ndarray)
assert np.ma.allequal(res, data_m)
def find_observations(self) -> Optional[Dict[str, Var]]:
"""If there are observations available, return them as a dictionary."""
if self.model is None:
return None
observations = {}
for obs in self.model.observed_RVs:
aux_obs = getattr(obs.tag, "observations", None)
if aux_obs is not None:
try:
obs_data = extract_obs_data(aux_obs)
observations[obs.name] = obs_data
except TypeError:
warnings.warn(f"Could not extract data from symbolic observation {obs}")
else:
warnings.warn(f"No data for observation {obs}")
return observations
def log_likelihood_vals_point(self, point, var, log_like_fun):
"""Compute log likelihood for each observed point."""
# TODO: This is a cheap hack; we should filter-out the correct
# variables some other way
point = {i.name: point[i.name] for i in log_like_fun.f.maker.inputs if i.name in point}
log_like_val = np.atleast_1d(log_like_fun(point))
if isinstance(var.owner.op, (AdvancedIncSubtensor, AdvancedIncSubtensor1)):
try:
obs_data = extract_obs_data(var.tag.observations)
except TypeError:
warnings.warn(f"Could not extract data from symbolic observation {var}")
mask = obs_data.mask
if np.ndim(mask) > np.ndim(log_like_val):
mask = np.any(mask, axis=-1)
log_like_val = np.where(mask, np.nan, log_like_val)
return log_like_val
